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@ARTICLE{Chen:1030925,
      author       = {Chen, Pansheng and An, Lijun and Wulan, Naren and Zhang,
                      Chen and Zhang, Shaoshi and Ooi, Leon Qi Rong and Kong, Ru
                      and Chen, Jianzhong and Wu, Jianxiao and Chopra, Sidhant and
                      Bzdok, Danilo and Eickhoff, Simon B. and Holmes, Avram J.
                      and Yeo, B. T. Thomas},
      title        = {{M}ultilayer meta-matching: {T}ranslating phenotypic
                      prediction models from multiple datasets to small data},
      journal      = {Imaging neuroscience},
      volume       = {2},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {FZJ-2024-05518},
      pages        = {1 - 22},
      year         = {2024},
      abstract     = {Resting-state functional connectivity (RSFC) is widely used
                      to predict phenotypic traits in individuals. Large sample
                      sizes can significantly improve prediction accuracies.
                      However, for studies of certain clinical populations or
                      focused neuroscience inquiries, small-scale datasets often
                      remain a necessity. We have previously proposed a
                      “meta-matching” approach to translate prediction models
                      from large datasets to predict new phenotypes in small
                      datasets. We demonstrated a large improvement over classical
                      kernel ridge regression (KRR) when translating models from a
                      single source dataset (UK Biobank) to the Human Connectome
                      Project Young Adults (HCP-YA) dataset. In the current study,
                      we propose two meta-matching variants (“meta-matching with
                      dataset stacking” and “multilayer meta-matching”) to
                      translate models from multiple source datasets across
                      disparate sample sizes to predict new phenotypes in small
                      target datasets. We evaluate both approaches by translating
                      models trained from five source datasets (with sample sizes
                      ranging from 862 participants to 36,834 participants) to
                      predict phenotypes in the HCP-YA and HCP-Aging datasets. We
                      find that multilayer meta-matching modestly outperforms
                      meta-matching with dataset stacking. Both meta-matching
                      variants perform better than the original “meta-matching
                      with stacking” approach trained only on the UK Biobank.
                      All meta-matching variants outperform classical KRR and
                      transfer learning by a large margin. In fact, KRR is better
                      than classical transfer learning when less than 50
                      participants are available for finetuning, suggesting the
                      difficulty of classical transfer learning in the very small
                      sample regime. The multilayer meta-matching model is
                      publicly available at
                      $https://github.com/ThomasYeoLab/Meta_matching_models/tree/main/rs-fMRI/v2.0.$},
      cin          = {INM-7},
      ddc          = {050},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001525523700001},
      doi          = {10.1162/imag_a_00233},
      url          = {https://juser.fz-juelich.de/record/1030925},
}